摘要： 高分辨率遥感影像细节丰富，具有类内差异大、类间差异不明显的特点。为此，模拟人的目视解译方式，提出一种基于共享特征的多级二叉树分类算法，把多类分类问题划分为多个两类分类问题，每级两类分类都提取共享特征，仅解译一类目标，已解译的类别不再参加后面的分类，利用这样的逐步淘汰机制完成一幅遥感影像的全部解译。实验结果表明，与K近邻、支持向量机等其他多类分类算法相比，该算法具有更高的分类精度。

关键词: 共享特征, 二叉树多级分类算法, GentleBoost算法, 二分分类器, 面向对象分类, 高分辨率遥感影像

Abstract: High resolution remote sensing images with abundant details generally have characteristics of great within class differences and unobvious between class differences. Simulating the visual interpretation, this paper proposes a multi-stage binary tree-structured classification algorithm based on sharing features. The multi-class classification problem is divided into multiple binary classification problems, sharing features are extracted to interpret objects of only one class at each binary classification stage, and each interpreted class will not participate in later classification. The proposed method makes use of the phase-out mechanism to complete the whole interpretation of a remote sensing image. Experimental results show that this algorithm has higher classification accuracy compared with other multi-class classification algorithms like K Nearest Neighbor(KNN), Support Vector Machine(SVM) and so on.

Key words: sharing features, multi-stage binary tree-structured classification algorithm, GentleBoost algorithm, binary classifier, object-oriented classification, high resolution remote sensing image

中图分类号: 

• TP391